76 to 100 of 380 LangChain Jobs in London

systems. Excellent analytical and problem-solving skills. Effective communication of complex ideas. Ability to work independently and collaboratively. Preferred Skills: Experience building scalable applications with LLMs using frameworks like LangChain, LlamaIndex, Hugging Face, etc. Deep knowledge of RAG implementation and enhancements. Benefits & perks (UK full-time employees): Generous PTO and holidays. Comprehensive medical and dental insurance. Paid parental leave More ❯

GPT, LLaMA, Claude, or similar, including fine-tuning, prompt engineering, and deploying models in production environments. Strong proficiency in Python, with expertise in using frameworks like Hugging Face Transformers, LangChain, OpenAI APIs, or other LLM orchestration tools. A solid understanding of tokenization, embedding models, vector databases (e.g., Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) pipelines. Experience designing and evaluating More ❯

GPT, LLaMA, Claude, or similar, including fine-tuning, prompt engineering, and deploying models in production environments. Strong proficiency in Python, with expertise in using frameworks like Hugging Face Transformers, LangChain, OpenAI APIs, or other LLM orchestration tools. A solid understanding of tokenization, embedding models, vector databases (e.g., Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) pipelines. Experience designing and evaluating More ❯

NOI/IOI, Top Coder, Kaggle and other competitions are preferred. Strong analytical and statistical modeling skills. Experience with machine learning (Generative AI) frameworks (e.g., scikit-learn, TensorFlow, PyTorch, Langchain, Weaviate, Langgraph, LlamaIndex). Proven track record of applying data science to solve real-world problems. Excellent communication and collaboration skills. Why AI71: Proven performance of our large language models More ❯

GPT, LLaMA, Claude, or similar, including fine-tuning, prompt engineering, and deploying models in production environments. Strong proficiency in Python, with expertise in using frameworks like Hugging Face Transformers, LangChain, OpenAI APIs, or other LLM orchestration tools. A solid understanding of tokenization, embedding models, vector databases (e.g., Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) pipelines. Experience designing and evaluating More ❯

systems, management products, or business applications. Full-stack development experience with AI technologies and their application to user or backend solutions. Experience with AI tools such as SageMaker, Vert.x, LangChain, Large Language Models, Prompt Engineering, DialogFlow, Python. Proficiency with front-end technologies (React, Angular, SwiftUI, Kotlin) and back-end technologies (Java, Typescript, Spring, Express). Experience with cloud platforms like More ❯

have strong foundational software engineering knowledge, including data structures, algorithms, system design, and OOP. You have advanced knowledge of LLM architectures and ML/DL frameworks (e.g. TensorFlow, PyTorch, LangChain, Keras, scikit-learn). You're ready to design, deploy and maintain production-grade Machine Learning systems. You're willing to champion best practices in code quality, testing, observability and More ❯

and experience fine-tuning them for specific tasks. Demonstrable experience in utilising different model architectures and training techniques to optimize performance. Familiarity with prompt engineering techniques and frameworks like LangChain, LlamaIndex, or DSpy. Good understanding of LLM models, including other components like VectorDBs and document loaders. Strong analytical and problem-solving skills, with the ability to work with complex datasets More ❯

to the table: 4+ years of hands-on experience in building and deploying ML models in production environments. Strong proficiency in Python and popular ML Frameworks such as PyTorch, LangChain, Agents. Experience with AWS Cloud, Kubernetes, ArgoCD, Docker, Terraform, Jenkins and strong understanding of CI/CD pipelines for ML and model deployment best practices. Experience with monitoring ML models More ❯

pipelines for continuous model improvement Collaborate with cross-functional teams-research, product, and engineering-to embed AI capabilities into products and services Evaluate and select appropriate AI frameworks (e.g., LangChain, LlamaIndex) to integrate agent components seamlessly with enterprise systems Build full-stack applications (front-end interfaces and back-end APIs) using modern languages and frameworks (React/Angular, Python, Java More ❯

customer requirements specifically for coding Ability to tune LLMs to provide required outputs, such as modifying Temperature, Top P, and Top K values Knowledge of AI frameworks such as Langchain or Llama Experience with AI embedding models such as Nomic or Cohere Strong proficiency in Python (and experience with AI libraries) History of delivering high-cadence modern applications with applied More ❯

to use them. Familiarity with trunk-based development in Git. Experience with back-end web application frameworks (Django, FastAPI, etc.). Experience with language models and agentic workflows (e.g., Langchain, AutoGPT). Knowledge of model evaluation metrics and techniques. Google Cloud (Cloud Functions, AppEngine, PubSub, Stackdriver, etc.). Docker, Kubernetes. Typescript, NodeJS, React, Webpack, etc. Java, C#. Experience within the More ❯

including the development of high-quality prompts and templates to guide the behavior and responses of LLMs. - experience with open source frameworks for building applications powered by LLMs like LangChain, LlamaIndex, and/or similar tools - Experience building generative AI applications on AWS using services such as Amazon Bedrock and Amazon SageMaker - Strong communication skills, with attention to detail and More ❯

e.g., AWS, Azure, GCP) and demonstrated proficiency in deploying applications within cloud environments. Generative AI Ecosystem Knowledge : Deep understanding of the generative AI ecosystem, including AI orchestration frameworks (e.g., LangChain, Llama Index, Haystack) and cloud provider AI offerings (e.g., AWS Bedrock, Vertex AI, Azure Machine Learning). Data Expertise : Strong foundation in data engineering, data analytics, or data science, with More ❯

AI Agents Solution Architect - Future Talent (Q2/Q3 opportunities) for leading GSI in the software engineering market AI Solution Architect to design and implement AI-driven agent solutions for the online commerce space. Deep understanding of AI/ML More ❯

engineering role that blends software development, AI experimentation, and product innovation. The successful candidate will play a key role in architecting agent-based systems, leveraging advanced frameworks such as LangChain and LlamaIndex, and deploying real-time AI capabilities that integrate deeply with SDLC processes. Key Responsibilities LLM Agent Development: Design and implement intelligent, multi-step agents using frameworks like LangChain … internal workshops, demos, and documentation initiatives to promote AI capabilities across teams and client projects. Ideal Candidate Profile Agentic Experience: Proven experience building agent workflows using frameworks such as LangChain, LangGraph, or equivalent. AI/ML Expertise: Solid understanding of large language models (LLMs), embeddings, vector search, and prompt engineering. Software Engineering Skills: Strong programming skills in Python (AI pipelines More ❯

Collaborate closely with IT, engineering, and operations teams to understand business needs and translate them into technical solutions. Build intelligent automation pipelines using tools such as Python, OpenAI APIs, LangChain, and n8n. Apply Large Language Models (LLMs) to real-world challenges like email triage, document parsing, and data enrichment. Implement retrieval-augmented generation (RAG) to power internal AI assistants with … For: Demonstrable experience in a Data Scientist or AI Automation role. Hands-on expertise with natural language processing (NLP) and Large Language Models (LLMs). Experience using Python, OpenAI, LangChain, and vector databases. Familiarity with automation platforms like n8n, Zapier, or similar. Solid understanding of retrieval-augmented generation (RAG) methods. Strong problem-solving skills and the ability to design practical More ❯

Collaborate closely with IT, engineering, and operations teams to understand business needs and translate them into technical solutions. Build intelligent automation pipelines using tools such as Python, OpenAI APIs, LangChain, and n8n. Apply Large Language Models (LLMs) to real-world challenges like email triage, document parsing, and data enrichment. Implement retrieval-augmented generation (RAG) to power internal AI assistants with … For: Demonstrable experience in a Data Scientist or AI Automation role. Hands-on expertise with natural language processing (NLP) and Large Language Models (LLMs). Experience using Python, OpenAI, LangChain, and vector databases. Familiarity with automation platforms like n8n, Zapier, or similar. Solid understanding of retrieval-augmented generation (RAG) methods. Strong problem-solving skills and the ability to design practical More ❯

modelling and deploying LLMs - experience with RAG, IR, NER etc would also be very beneficial Strong programming skills (e.g., Python) and experience with modern ML frameworks (e.g., PyTorch, TensorFlow, LangChain). Collaborating with other Researchers, Product, Engineering and Business Stakeholders in an agile manner to demonstrate value and iterate with customer feedback. Please apply below for immediate consideration More ❯

modelling and deploying LLMs - experience with RAG, IR, NER etc would also be very beneficial Strong programming skills (e.g., Python) and experience with modern ML frameworks (e.g., PyTorch, TensorFlow, LangChain). Collaborating with other Researchers, Product, Engineering and Business Stakeholders in an agile manner to demonstrate value and iterate with customer feedback. Please apply below for immediate consideration More ❯

modelling and deploying LLMs - experience with RAG, IR, NER etc would also be very beneficial Strong programming skills (e.g., Python) and experience with modern ML frameworks (e.g., PyTorch, TensorFlow, LangChain). Collaborating with other Researchers, Product, Engineering and Business Stakeholders in an agile manner to demonstrate value and iterate with customer feedback. Please apply below for immediate consideration More ❯

modelling and deploying LLMs - experience with RAG, IR, NER etc would also be very beneficial Strong programming skills (e.g., Python) and experience with modern ML frameworks (e.g., PyTorch, TensorFlow, LangChain). Collaborating with other Researchers, Product, Engineering and Business Stakeholders in an agile manner to demonstrate value and iterate with customer feedback. Please apply below for immediate consideration More ❯

effectiveness, and sustainability Guide junior team members and improve engineering best practices Your skillset: Expert Python dev (pandas, FastAPI, Pydantic) Strong ML/AI experience incl. AutoML, LLMs, HuggingFace, LangChain Proficiency in Linux, Git, PostgreSQL, and API development Experience deploying AI models in containerised, cloud-based environments Bonus: agentic AI (smolagents, AutoGen), fine-tuning, MLOps know-how If you're More ❯

You will collaborate with multidisciplinary teams to ensure scalable, secure, and compliant AI solutions that enhance business workflows. Key Responsibilities: Develop and optimize RAG pipelines using frameworks such as LangChain, LlamaIndex, or Haystack. Build data ingestion workflows including OCR, chunking, embedding, and semantic search integration. Integrate vector databases such as FAISS, Pinecone, or Qdrant into AI workflows. Deliver scalable GenAI More ❯

Model Deployment & Inferencing : Optimise model serving and inference using vLLM, DeepSpeed, TensorRT, Triton, and other acceleration frameworks. Multi-Agent Systems : Develop and integrate agentic capabilities using frameworks such as LangChain, CrewAI, AutoGen, and DSPy. AWS Cloud & MLOps: Deploy scalable machine learning workloads on AWS using services like SageMaker, Bedrock, Lambda, S3, DynamoDB, ECS, and EKS. End-to-End AI Product More ❯